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About This Role
Responsibilities:
MaxLinear is seeking a highly motivated Artificial Intelligence Engineer to evaluate and integrate cutting\-edge AI technologies into the semiconductor engineering workflows. This role sits at the intersection of AI innovation, engineering productivity, and enterprise\-scale deployment, helping shape how modern AI systems are operationalized across teams. You will play a critical role in identifying high\-impact use cases, benchmarking AI platforms, and delivering scalable, secure solutions that drive measurable business value. In this role, you will focus on the following:
- Evaluate AI platforms and large language models (LLMs), including GPT, Claude, Gemini, and SWE\-class systems
- Contribute to the design and execution of benchmarks to measure quality of results (QoR), productivity gains, and scalability
- Identify strengths, limitations, and optimal use cases across AI tools and architectures
- Partner with cross\-functional teams to deploy solutions across multiple domains
- Validate and implement AI\-driven solutions within real\-world semiconductor engineering workflows
- Partner with engineering to translate engineering use cases into production\-ready AI capabilities
- Partner with internal teams to develop and analyze ROI and cost\-performance tradeoffs across cloud and on\-prem deployment models
- Provide data\-driven recommendations on AI platform selection and scaling strategies
- Optimize resource utilization for maximum business impact
- Design and support LLM deployments across hybrid environments (cloud and on\-prem)
- Integrate with enterprise data platforms, including data lakes and MCP gateway architectures
- Ensure performance, reliability, and scalability of deployed AI systems
- Implement best practices based on NIST and OWASP frameworks
- Define AI usage guardrails, governance models, and compliance policies
- Ensure enterprise\-grade security and responsible AI adoption
Qualifications:
- Experience working with large language models (LLMs) and AI/ML systems
- Strong analytical skills with experience in benchmarking, evaluation, or performance analysis
- Experience with cloud platforms (AWS, Azure, GCP) and/or on\-prem infrastructure
- Ability to translate technical capabilities into business value
- Experience integrating AI into engineering or semiconductor workflows
- Familiarity with data platforms, pipelines, and large\-scale data processing
- Knowledge of AI security, governance, and compliance frameworks
- Hands\-on experience with model deployment, APIs, and system integration
- BS in Electrical Engineering or related
Compensation and Benefits:
MaxLinear has a Total Compensation philosophy which includes base salary and annual discretionary bonus eligibility and many positions also include stock\-based compensation.
MaxLinear's good faith estimate starting base salary range is: $75,000 to $102,000 Annually
We offer competitive benefits designed to support employee health, welfare, and retirement and some highlights are: health care benefits, 401k savings plan, Employee Stock Purchase Plan (ESPP), and paid time off.
The actual starting base salary will be determined by the match to certain role\-related criteria such as educational degree(s) or equivalent, relevant work experience, skillset needed for the role, and geographic location; this is not an all\-inclusive list as some roles may require unique skills or experience.
Qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on race, sex, religion, national origin, sexual orientation, gender identity, disability, or protected veteran status.
Company Overview:
MaxLinear is a global, NASDAQ\-traded company (MXL) where the entrepreneurial spirit is alive and well. We are a fabless system\-on\-chip product company, striving to improve the world’s communication networks for everyone through our highly integrated radio\-frequency (RF), analog, digital, and mixed\-signal semiconductor solutions for access and connectivity, wired and wireless infrastructure, and industrial and multi\-market applications.
We hire the best people in the industry and engage them in some of the most exciting opportunities that connect the world we live in today. Our growth has come from innovative, bold approaches to solving some of the world’s most challenging communication technology problems in the most efficient and effective manner.
MaxLinear began by developing the world’s first high\-performance TV tuner chip using standard CMOS process technology. Others said we couldn’t achieve the extremely high\-performance requirements using CMOS, but we proved them wrong and achieved enduring global market leadership with our designs. Since then, we’ve developed a full line of products that drive 4G and 5G infrastructure; enable data center, metro and long\-haul optical interconnects; bring 10Gbit to the home; power the IoT revolution; and enable robust and reliable communication in harsh industrial environments. Over the years, we’ve expanded through organic growth and through several acquisitions that have perfectly complemented our existing portfolio and enabled us to deliver complete end\-to\-end solutions in our target markets. One such example was the acquisition of Intel’s Home Gateway Platform Division that added Wi\-Fi, Ethernet, and Broadband Gateway Processor SoC technology to our connected home portfolio creating a complete and scalable platform of connectivity and access solutions to fully address our customers’ needs.
Our headquarters are in Carlsbad, near San Diego, California. We also have major design centers in Irvine and San Jose, California; Valencia, Spain; Bangalore, India; Munich, Germany; Israel; and Singapore.
We have approximately 1,200 employees, a substantial majority of whom have engineering degrees and include masters and Ph.D. graduates from many of the premiere universities around the world. Our employees thrive on innovation, outstanding execution, outside\-the\-box thinking, nimbleness, and collaboration. Together, we form a high\-energy business team that is focused on building the best and most innovative products on the market.
Salary Context
This $75K-$102K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At MaxLinear Inc., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($88K) sits 51% below the category median. Disclosed range: $75K to $102K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
MaxLinear Inc. AI Hiring
MaxLinear Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $102K - $102K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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